FlowNL: Asking the Flow Data in Natural Languages

Huang, Jieying; Yang, Xi; Hu, Junnan; Tao, Jun

doi:10.1109/tvcg.2022.3209453

Cited by 10 publications

(26 citation statements)

References 45 publications

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“…In the 20 articles identified by our search strategy (Section 3.3), we included 10 articles that are chatbot-based [63,[80][81][82][83]86,87,89,91,92] and 10 articles that are form-based NLIs but have some chatbot characteristics such as providing feedback [15,39,40,62,64,69,70,84,88,90].…”

Section: Resultsmentioning

confidence: 99%

“…The challenges of both understandability and discoverability require an interactive conversational system to guide the users on how to effectively communicate their goals (also referred to as intentions). Well-known Conversational Guidance strategies are based on help-the chatbot gives the users hints on what to ask; intent auto-complete functions-the system makes suggestions of possible intents while the users are writing the intent [62,[68][69][70]; and intent recommendations [40]-after giving a response, the system suggests, based on data or on the previous turns of the analytical conversation, possible next intents to the users. Additionally, the understandability problem of NLIs is mainly derived from the biggest challenge that NL poses, which is ambiguity.…”

Section: Inputmentioning

confidence: 99%

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Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review

2023

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Rapid growth in the generation of data from various sources has made data visualisation a valuable tool for analysing data. However, visual analysis can be a challenging task, not only due to intricate dashboards but also when dealing with complex and multidimensional data. In this context, advances in Natural Language Processing technologies have led to the development of Visualisation-oriented Natural Language Interfaces (V-NLIs). In this paper, we carry out a scoping review that analyses synergies between the fields of Data Visualisation and Natural Language Interaction. Specifically, we focus on chatbot-based V-NLI approaches and explore and discuss three research questions. The first two research questions focus on studying how chatbot-based V-NLIs contribute to interactions with the Data and Visual Spaces of the visualisation pipeline, while the third seeks to know how chatbot-based V-NLIs enhance users’ interaction with visualisations. Our findings show that the works in the literature put a strong focus on exploring tabular data with basic visualisations, with visual mapping primarily reliant on fixed layouts. Moreover, V-NLIs provide users with restricted guidance strategies, and few of them support high-level and follow-up queries. We identify challenges and possible research opportunities for the V-NLI community such as supporting high-level queries with complex data, integrating V-NLIs with more advanced systems such as Augmented Reality (AR) or Virtual Reality (VR), particularly for advanced visualisations, expanding guidance strategies beyond current limitations, adopting intelligent visual mapping techniques, and incorporating more sophisticated interaction methods.

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Section: Resultsmentioning

confidence: 99%

Section: Inputmentioning

confidence: 99%

Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review

2023

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“…Snowy [23] and QRec-NLI [24] recommend utterances in NL-based visual analysis, leveraging NL to provide analytical guidance. In recent years, NLIs have been applied to various scenarios and tasks, such as flow data exploration [25], visualization authoring [26], and comparative analysis [27]. Other studies [28], [29], [30], [31] build query corpora for linguistic property analysis and construct labeled benchmark datasets for NLI evaluation and deep learning model training [32], [33].…”

Section: Related Work 21 Nli For Data Visualizationmentioning

confidence: 99%

XNLI: Explaining and Diagnosing NLI-Based Visual Data Analysis

Feng

Wang

Pan

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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Natural language interfaces (NLIs) enable users to flexibly specify analytical intentions in data visualization. However, diagnosing the visualization results without understanding the underlying generation process is challenging. Our research explores how to provide explanations for NLIs to help users locate the problems and further revise the queries. We present XNLI, an explainable NLI system for visual data analysis. The system introduces a Provenance Generator to reveal the detailed process of visual transformations, a suite of interactive widgets to support error adjustments, and a Hint Generator to provide query revision hints based on the analysis of user queries and interactions. Two usage scenarios of XNLI and a user study verify the effectiveness and usability of the system. Results suggest that XNLI can significantly enhance task accuracy without interrupting the NLI-based analysis process.

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“…For instance, a natural language prompt such as "get data from earnings of the fourth quarter relative to our competitors" offers ample ambiguity and opportunity for the model to exert agency, such as in identifying who the competitors are, how to compute earnings data, which business units to include, and so on. Huang et al employ this approach for flow visualization using NLP [HXHT22]. These moments of agency invite risk (e.g., the model might provide bad results or exhibit biases) [GSS * 22], but they also invite opportunity.…”

Section: Data-fyingmentioning

confidence: 99%

Doom or Deliciousness: Challenges and Opportunities for Visualization in the Age of Generative Models

Schetinger¹,

Bartolomeo²,

El‐Assady³

et al. 2023

Preprint

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Generative text-to-image models (as exemplified by DALL-E, MidJourney, and Stable Diffusion) have recently made enormous technological leaps, demonstrating impressive results in many graphical domains—from logo design to digital painting and photographic composition. However, the quality of these results has led to existential crises in some fields of art, leading to questions about the role of human agency in the production of meaning in a graphical context. Such issues are central to visualization, and while these generative models have yet to be widely applied to visualization, it seems only a matter of time until their integration is manifest. Seeking to circumvent similar ponderous dilemmas, we attempt to understand the roles that generative models might play across visualization. We do so by constructing a framework that characterizes what these technologies offer at various stages of the visualization workflow, augmented and analyzed through semi-structured interviews with 21 experts from related domains. Throughthis work, we map the space of opportunities and risks that might arise in this intersection, identifying doomsday prophecies and delicious low-hanging fruits that are ripe for research.

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FlowNL: Asking the Flow Data in Natural Languages

Cited by 10 publications

References 45 publications

Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review

Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review

XNLI: Explaining and Diagnosing NLI-Based Visual Data Analysis

Doom or Deliciousness: Challenges and Opportunities for Visualization in the Age of Generative Models

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